The cell architectures for the best configuration (Mel-Fixed) is illustrated in Fig. REF . We observed that, even though architecture parameters are randomly initialised, after several warm-up epochs, those for dilated convolution operations tend to dominate. This may indicated that, compared to other candidate operations within the search space, dilated convolutions contribute more to representation learning when applied to raw waveforms. Dilated convolutions act to increase the receptive field [1]}, [2]}, [3]}. The use of greater contextual information then helps to improve performance.
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